8 research outputs found
Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples
Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell disease -- patients whose subjective experiences of pain were tracked for several months via a mobile app. We find that different methods for aligning irregularly sampled sparse data sets can lead to different optimal numbers of clusters, even for synthetic data with known properties. For the case of sickle cell disease, we find that three clusters is a reasonable choice, and these appear to correspond to (1) a low pain group with occasionally acute pain, (2) a group which experiences moderate mean pain that fluctuates often from low to high, and (3) a group that experiences persistent high levels of pain. Our results may help physicians and patients better understand and manage patients\u27 pain levels over time, and we expect that the methods we develop will apply to a wide range of other data sources in medicine and beyond
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate
Terror attacks have been linked in part to online extremist content. Although
tens of thousands of Islamist extremism supporters consume such content, they
are a small fraction relative to peaceful Muslims. The efforts to contain the
ever-evolving extremism on social media platforms have remained inadequate and
mostly ineffective. Divergent extremist and mainstream contexts challenge
machine interpretation, with a particular threat to the precision of
classification algorithms. Our context-aware computational approach to the
analysis of extremist content on Twitter breaks down this persuasion process
into building blocks that acknowledge inherent ambiguity and sparsity that
likely challenge both manual and automated classification. We model this
process using a combination of three contextual dimensions -- religion,
ideology, and hate -- each elucidating a degree of radicalization and
highlighting independent features to render them computationally accessible. We
utilize domain-specific knowledge resources for each of these contextual
dimensions such as Qur'an for religion, the books of extremist ideologues and
preachers for political ideology and a social media hate speech corpus for
hate. Our study makes three contributions to reliable analysis: (i) Development
of a computational approach rooted in the contextual dimensions of religion,
ideology, and hate that reflects strategies employed by online Islamist
extremist groups, (ii) An in-depth analysis of relevant tweet datasets with
respect to these dimensions to exclude likely mislabeled users, and (iii) A
framework for understanding online radicalization as a process to assist
counter-programming. Given the potentially significant social impact, we
evaluate the performance of our algorithms to minimize mislabeling, where our
approach outperforms a competitive baseline by 10.2% in precision.Comment: 22 page
Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits
Pain in sickle cell disease (SCD) is often associated with increased
morbidity, mortality, and high healthcare costs. The standard method for
predicting the absence, presence, and intensity of pain has long been
self-report. However, medical providers struggle to manage patients based on
subjective pain reports correctly and pain medications often lead to further
difficulties in patient communication as they may cause sedation and
sleepiness. Recent studies have shown that objective physiological measures can
predict subjective self-reported pain scores for inpatient visits using machine
learning (ML) techniques. In this study, we evaluate the generalizability of ML
techniques to data collected from 50 patients over an extended period across
three types of hospital visits (i.e., inpatient, outpatient and outpatient
evaluation). We compare five classification algorithms for various pain
intensity levels at both intra-individual (within each patient) and
inter-individual (between patients) level. While all the tested classifiers
perform much better than chance, a Decision Tree (DT) model performs best at
predicting pain on an 11-point severity scale (from 0-10) with an accuracy of
0.728 at an inter-individual level and 0.653 at an intra-individual level. The
accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e.,
no/mild pain: 0-5, severe pain: 6-10) at an intra-individual level. Our
experimental results demonstrate that ML techniques can provide an objective
and quantitative evaluation of pain intensity levels for all three types of
hospital visits.Comment: Accepted for presentation at the FIRST WORKSHOP ON COMPUTATIONAL &
AFFECTIVE INTELLIGENCE IN HEALTHCARE APPLICATIONS (VULNERABLE POPULATIONS) In
Conjunction with the International Conference on Pattern Recognition (ICPR)
202
Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits
Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from 50 patients over an extended period across three types of hospital visits (i.e., inpatient, outpatient and outpatient evaluation). We compare five classification algorithms for various pain intensity levels at both intra-individual (within each patient) and inter-individual (between patients) level. While all the tested classifiers perform much better than chance, a Decision Tree (DT) model performs best at predicting pain on an 11-point severity scale (from 0–10) with an accuracy of 0.728 at an inter-individual level and 0.653 at an intra-individual level. The accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e., no/mild pain: 0–5, severe pain: 6–10) at an inter-individual level. Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits
Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples
Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell disease -- patients whose subjective experiences of pain were tracked for several months via a mobile app. We find that different methods for aligning irregularly sampled sparse data sets can lead to different optimal numbers of clusters, even for synthetic data with known properties. For the case of sickle cell disease, we find that three clusters is a reasonable choice, and these appear to correspond to (1) a low pain group with occasionally acute pain, (2) a group which experiences moderate mean pain that fluctuates often from low to high, and (3) a group that experiences persistent high levels of pain. Our results may help physicians and patients better understand and manage patients\u27 pain levels over time, and we expect that the methods we develop will apply to a wide range of other data sources in medicine and beyond
Pain Intensity Assessment in Sickle Cell Disease Patients Using Vital Signs During Hospital Visits
Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from 50 patients over an extended period across three types of hospital visits (i.e., inpatient, outpatient and outpatient evaluation). We compare five classification algorithms for various pain intensity levels at both intra-individual (within each patient) and inter-individual (between patients) level. While all the tested classifiers perform much better than chance, a Decision Tree (DT) model performs best at predicting pain on an 11-point severity scale (from 0–10) with an accuracy of 0.728 at an inter-individual level and 0.653 at an intra-individual level. The accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e., no/mild pain: 0–5, severe pain: 6–10) at an inter-individual level. Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits
Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples
Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell disease -- patients whose subjective experiences of pain were tracked for several months via a mobile app. We find that different methods for aligning irregularly sampled sparse data sets can lead to different optimal numbers of clusters, even for synthetic data with known properties. For the case of sickle cell disease, we find that three clusters is a reasonable choice, and these appear to correspond to (1) a low pain group with occasionally acute pain, (2) a group which experiences moderate mean pain that fluctuates often from low to high, and (3) a group that experiences persistent high levels of pain. Our results may help physicians and patients better understand and manage patients\u27 pain levels over time, and we expect that the methods we develop will apply to a wide range of other data sources in medicine and beyond